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Table Lookups
In this tutorial, we will review the ways to perform direct table lookups in Concrete-Numpy.
Concrete-Numpy provides a
LookupTable class for you to create your own tables and apply them in your circuits.LookupTables can have any number of elements. Let's call them N. As long as the lookup variable is in range [-N, N), table lookup is valid.If you go out of bounds of this range, you will get the following error:
IndexError: index 10 is out of bounds for axis 0 with size 6
The number of elements in the lookup table doesn't affect performance in any way.
You can create the lookup table using a list of integers and apply it using indexing:
import concrete.numpy as cnp
table = cnp.LookupTable([2, -1, 3, 0])
@cnp.compiler({"x": "encrypted"})
def f(x):
return table[x]
inputset = range(4)
circuit = f.compile(inputset)
assert circuit.encrypt_run_decrypt(0) == table[0] == 2
assert circuit.encrypt_run_decrypt(1) == table[1] == -1
assert circuit.encrypt_run_decrypt(2) == table[2] == 3
assert circuit.encrypt_run_decrypt(3) == table[3] == 0
When you apply the table lookup to a tensor, you apply the scalar table lookup to each element of the tensor:
import concrete.numpy as cnp
import numpy as np
table = cnp.LookupTable([2, -1, 3, 0])
@cnp.compiler({"x": "encrypted"})
def f(x):
return table[x]
inputset = [np.random.randint(0, 4, size=(2, 3)) for _ in range(10)]
circuit = f.compile(inputset)
sample = [
[0, 1, 3],
[2, 3, 1],
]
expected_output = [
[2, -1, 0],
[3, 0, -1],
]
actual_output = circuit.encrypt_run_decrypt(np.array(sample))
for i in range(2):
for j in range(3):
assert actual_output[i][j] == expected_output[i][j] == table[sample[i][j]]
LookupTable mimics array indexing in Python, which means if the lookup variable is negative, the table is looked up from the back:import concrete.numpy as cnp
table = cnp.LookupTable([2, -1, 3, 0])
@cnp.compiler({"x": "encrypted"})
def f(x):
return table[-x]
inputset = range(1, 5)
circuit = f.compile(inputset)
assert circuit.encrypt_run_decrypt(1) == table[-1] == 0
assert circuit.encrypt_run_decrypt(2) == table[-2] == 3
assert circuit.encrypt_run_decrypt(3) == table[-3] == -1
assert circuit.encrypt_run_decrypt(4) == table[-4] == 2
In case you want to apply a different lookup table to each element of a tensor, you can have a
LookupTable of LookupTables:import concrete.numpy as cnp
import numpy as np
squared = cnp.LookupTable([i ** 2 for i in range(4)])
cubed = cnp.LookupTable([i ** 3 for i in range(4)])
table = cnp.LookupTable([
[squared, cubed],
[squared, cubed],
[squared, cubed],
])
@cnp.compiler({"x": "encrypted"})
def f(x):
return table[x]
inputset = [np.random.randint(0, 4, size=(3, 2)) for _ in range(10)]
circuit = f.compile(inputset)
sample = [
[0, 1],
[2, 3],
[3, 0],
]
expected_output = [
[0, 1],
[4, 27],
[9, 0]
]
actual_output = circuit.encrypt_run_decrypt(np.array(sample))
for i in range(3):
for j in range(2):
if j == 0:
assert actual_output[i][j] == expected_output[i][j] == squared[sample[i][j]]
else:
assert actual_output[i][j] == expected_output[i][j] == cubed[sample[i][j]]
In this example, we applied a
squared table to the first column and a cubed table to the second one.Concrete-Numpy tries to fuse some operations into table lookups automatically, so you don't need to create the lookup tables manually:
import concrete.numpy as cnp
import numpy as np
@cnp.compiler({"x": "encrypted"})
def f(x):
return (42 * np.sin(x)).astype(np.int64) // 10
inputset = range(8)
circuit = f.compile(inputset)
for x in range(8):
assert circuit.encrypt_run_decrypt(x) == f(x)
All lookup tables need to be from integers to integers. So, without
.astype(np.int64), Concrete-Numpy will not be able to fuse.The function is first traced into:
Then, Concrete-Numpy fuses appropriate nodes:
Fusing makes the code more readable and easier to modify, so try to utilize it over manual
LookupTables as much as possible.Last modified 9mo ago